基于行为关键语句特征的停车场异常行为识别方法

doi:10.11896/jsjkx.180901750

Abstract

Abstract: With the development of technology and the popularity of cameras,people’s demands on intelligent video surveillance are increasing.Anomaly behavior recognition is a key part of intelligent monitoring systems and plays an important role in maintaining social security.Aiming at the spatio-temporal feature of video data,this paper proposed a method of characterizing behavior as a key sentence with time series,termed Key Sentence of Behavior Sequence (KSBS),and realized the anomaly behavior recognition in the parking scenes by learning key sentences of behaviors.Firstly,the motion sequence is segmented,the foreground target is extracted,and the Motion Period Curve (MPC) of the foreground target is calculated.Then,according to the motion cycle curve,the MPC and DTW method are used to extract the behavior key frames.Finally,based on the semantic understanding method in the field of natural language proces-sing,the behavior key frames are characterized as a series of behavior key sentence.In light of time series features of key sentences,LSTM,which is expert in dealing with time series data,is used to classify the key statements of behaviors.In order to solve the existing data imbalance problem,GAN is used to expand the training set,thus increasing the sample space and balancing the difference between different types of data.Validation results on CASIA behavior database and self-built behavior database show that the average recognition rate of the proposed method for anomaly behavior is 97%.It is proved that the Key Sentece of Behavior Sequence can better represent the behavior information and the LSTM model is more suitable for learning the patterns behind the time series data,verifying the effectiveness of the proposed method on anomaly behavior recognition in parking scenes.

Key words: Anomaly behavior recognition, Dynamic time warping, Features of deep learning, Generative adversarial networks, Long Short-term memory neural network

CLC Number:

TP391

WANG Hong-nian, SU Han, LONG Gang, WANG Yan-fei, YIN Kuan. Parking Anomaly Behavior Recognition Method Based on Key Sentence of Behavior Sequence Features[J].Computer Science, 2019, 46(10): 299-306.

References

[1]FAN Z,LING S,JIN X,et al.From handcrafted to learned representations for human action recognition:A survey[J].Image and Vision Computing,2016,55(P2):42-52.
[2]ZOU J Y.Research on abnormal activity recognition in parking[D].Chengdu:Sichuan Normal University,2014.(in Chinese)
邹佳运.停车场异常行为识别方法研究[D].成都:四川师范大学,2014.
[3]ZIVKOVIC Z,VAN DER HEIJDEN F.Efficient adaptive density estimation per image pixel for the task of background subtraction[J].Pattern Recognition Letters,2006,27(7):773-780.
[4]KIM K,CHALIDABHONGSE T H,HARWOOD D,et al.Real-time foreground-background segmentation using codebook model[J].Real-time Imaging,2005,11(3):172-185.
[5]BARNICH O,VAN DROOGENBROECK M.ViBe:A universal background subtraction algorithm for video sequences[J].IEEE Transactions on Image processing,2011,20(6):1709-1724.
[6]ZHANG D X,DAI K R.Adaptive Target Extraction and Trac-king Method for Complex Image Sequences[J].Chinese Journal of Electronics,1994,22(10):46-53.(in Chinese)
张天序,戴可荣.复杂图象序列的自适应目标提取和跟踪方法[J].电子学报,1994,22(10):46-53.
[7]BOBICK A F,DAVIS J W.The recognition of human movement using temporal templates[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2001,23(3):257-267.
[8]WANG Y,HUANG K,TAN T.Human activity recognition based on r transform[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.IEEE,2007:1-8.
[9]CHEN H S,CHEN H T,CHEN Y W,et al.Human action reco-gnition using star skeleton[C]//Proceedings of the 4th ACM International Workshop on Video Surveillance and Sensor Networks.ACM,2006:171-178.
[10]SOUVENIR R,BABBS J.Learning the viewpoint manifold for action recognition[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.IEEE,2008:1-7.
[11]GORELICK L,BLANK M,SHECHTMAN E,et al.Actions as space-time shapes[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2007,29(12):2247-2253.
[12]ERFANI S M,RAJASEGARAR S,KARUNASEKERA S,et al.High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning[J].Pattern Recognition,2016,58(C):121-134.
[13]LIU C,XU W S,WU Q D.Spatiotemporal Convolutional Neural Networks and its Application in Action Recognition[J].Computer Science,2015,42(7):245-249.(in Chinese)
刘琮,许维胜,吴启迪.时空域深度卷积神经网络及其在行为识别上的应用[J].计算机科学,2015,42(7):245-249.
[14]TRAN D,BOURDEV L,FERGUS R,et al.Learning spatiotemporal features with 3d convolutional networks[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.IEEE,2015:4489-4497.
[15]TRAN D,WANG H,TORRESANI L,et al.A Closer Look at Spatiotemporal Convolutions for Action Recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.IEEE,2018:6450-6459.
[16]SULTANI W,CHEN C,SHAH M.Real-world Anomaly Detection in Surveillance Videos[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.IEEE,2018:6479-6488.
[17]RAVANBAKHSH M,NABI M,SANGINETO E,et al.Abnormal event detection in videos using generative adversarial nets[C]//IEEE International Conference on Image Processing.IEEE,2017:1577-1581.
[18]KAR A,RAI N,SIKKA K,et al.Adascan:Adaptive scan pooling in deep convolutional neural networks for human action reco-gnition in videos[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.IEEE,2017:3376-3385.
[19]GAO X.Research on abnormal behavior ofpedestrians in video surveillance [D].Chengdu:University of Electronic Science and Technology,2018.(in Chinese)
高翔.视频监控中行人异常行为分析研究[D].成都:电子科技大学,2018.
[20]WANG H N,SU H.STAR:A Concise Deep Learning Framework for Citywide Human Mobility Prediction [C]//IEEE International Conference on Mobile Data Management.IEEE,2019:304-309.
[21]KEOGH E J,PAZZANI M J.Derivative dynamic time warping[C]//Proceedings of the 2001 SIAM International Conference on Data Mining.Philadelphia:SIAM,2001:1-11.
[22]SU H,HUANG F G.A Method of Gait Recognition UsingSpatio-Temporal Analysis[J].Pattern Recognition & Artificial Intelligence,2007,20(2):281-286.(in Chinese)
苏菡,黄凤岗.一种基于时空分析的步态识别方法[J].模式识别与人工智能,2007,20(2):281-286.
[23]RATLIFF L J,BURDEN S A,SASTRY S S.Characterization and computation of localnash equilibria in continuous games[C]//Communication,Control,and Computing (Allerton).IEEE,2013:917-924.
[24]GOODFELLOW I,POUGET-ABADIE J,MIRZA M,et al.Ge-nerative adversarial nets[C]//Advances in neural information processing systems.New York:Curran Associates,2014:2672-2680.
[25]HOCHREITER S,SCHMIDHUBER J.Long short-term memory[J].Neural Computation,1997,9(8):1735-1780.
[26]ZHANG X R,JU X Z,SONG P,et al.Feature Fusion Based on DBN for Cross-Corpus Speech Emotion Recognition[J].Signal Processing,2017,33(5):649-660.(in Chinese)
张昕然,巨晓正,宋鹏,等.用于跨库语音情感识别的 DBN 特征融合方法[J].信号处理,2017,33(5):649-660.
[27]LECUN Y,BOTTOU L,BENGIO Y,et al.Gradient-based learning applied to document recognition[J].Proceedings of the IEEE,1998,86(11):2278-2324.

Related Articles 15

[1]	XU Guo-ning, CHEN Yi-peng, CHEN Yi-ming, CHEN Jin-yin, WEN Hao. Data Debiasing Method Based on Constrained Optimized Generative Adversarial Networks [J]. Computer Science, 2022, 49(6A): 184-190.
[2]	XU Hui, KANG Jin-meng, ZHANG Jia-wan. Digital Mural Inpainting Method Based on Feature Perception [J]. Computer Science, 2022, 49(6): 217-223.
[3]	DOU Zhi, WANG Ning, WANG Shi-jie, WANG Zhi-hui, LI Hao-jie. Sketch Colorization Method with Drawing Prior [J]. Computer Science, 2022, 49(4): 195-202.
[4]	GAO Zhi-yu, WANG Tian-jing, WANG Yue, SHEN Hang, BAI Guang-wei. Traffic Prediction Method for 5G Network Based on Generative Adversarial Network [J]. Computer Science, 2022, 49(4): 321-328.
[5]	LI Si-quan, WAN Yong-jing, JIANG Cui-ling. Multiple Fundamental Frequency Estimation Algorithm Based on Generative Adversarial Networks for Image Removal [J]. Computer Science, 2022, 49(3): 179-184.
[6]	TAN Xin-yue, HE Xiao-hai, WANG Zheng-yong, LUO Xiao-dong, QING Lin-bo. Text-to-Image Generation Technology Based on Transformer Cross Attention [J]. Computer Science, 2022, 49(2): 107-115.
[7]	ZHANG Wei-qi, TANG Yi-feng, LI Lin-yan, HU Fu-yuan. Image Stream From Paragraph Method Based on Scene Graph [J]. Computer Science, 2022, 49(1): 233-240.
[8]	LIN Zhen-xian, ZHANG Meng-kai, WU Cheng-mao, ZHENG Xing-ning. Face Image Inpainting with Generative Adversarial Network [J]. Computer Science, 2021, 48(9): 174-180.
[9]	XU Tao, TIAN Chong-yang, LIU Cai-hua. Deep Learning for Abnormal Crowd Behavior Detection:A Review [J]. Computer Science, 2021, 48(9): 125-134.
[10]	PAN Xiao-qin, LU Tian-liang, DU Yan-hui, TONG Xin. Overview of Speech Synthesis and Voice Conversion Technology Based on Deep Learning [J]. Computer Science, 2021, 48(8): 200-208.
[11]	YE Hong-liang, ZHU Wan-ning, HONG Lei. Music Style Transfer Method with Human Voice Based on CQT and Mel-spectrum [J]. Computer Science, 2021, 48(6A): 326-330.
[12]	LIU Meng-yang, WU Li-juan, LIANG Hui, DUAN Xu-lei, LIU Shang-qing, GAO Yi-bo. A Kind of High-precision LSTM-FC Atmospheric Contaminant Concentrations Forecasting Model [J]. Computer Science, 2021, 48(6A): 184-189.
[13]	WANG Jian-ming, LI Xiang-feng, YE Lei, ZUO Dun-wen, ZHANG Li-ping. Medical Image Deblur Using Generative Adversarial Networks with Channel Attention [J]. Computer Science, 2021, 48(6A): 101-106.
[14]	HU Yu-jie, CHANG Jian-hui, ZHANG Jian. Image Synthesis with Semantic Region Style Constraint [J]. Computer Science, 2021, 48(2): 134-141.
[15]	YU Xiao-ming, HUANG Hua. Research on Application of Improved GAN Network in Generating Short Video [J]. Computer Science, 2021, 48(11A): 625-629.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

Comments

Recommended 0

No Suggested Reading articles found!

Parking Anomaly Behavior Recognition Method Based on Key Sentence of Behavior Sequence Features

PDF (PC)